Risk Classification for Interstitial Cystitis/Bladder Pain Syndrome (IC/BPS) Using Machine Learning Based Predictions

To improve diagnosis of IC/BPS(IC) we hereby developed an improved IC risk classification using machine learning algorithms. A national crowdsourcing resulted in 1,264 urine samples consisting of 536 IC (513 female, 21 male, 2 unspecified), and 728 age-matched controls (318 female, 402 male, 8 unspecified) with corresponding PRO pain and symptom scores.

Risk Classification for Interstitial Cystitis/Bladder Pain Syndrome (IC/BPS) Using Machine Learning Based Predictions - Beyond the Abstract

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Michael B. Chancellor, MD, Professor of Urology, Oakland University William Beaumont School of Medicine, Rochester, MI

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Risk Classification for Interstitial Cystitis/Bladder Pain Syndrome (IC/BPS) Using Machine Learning Based Predictions - Beyond the Abstract

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